Umbrella

Cloud computing enables users to perform distributed computing tasks on many virtual machines, without owning a physical cluster. Recently, various distributed computing tasks such as scientific applications are being moved from supercomputers and private clusters to public clouds. Message passing interface (MPI) is a key and common component in distributed computing tasks. The virtualized computing environment of the public cloud hides the network topology information from the users, and existing topology-aware optimizations for MPI are no longer feasible in the cloud environment. We propose a network performance aware MPI library named CMPI. CMPI embraces a new model for capturing the network performance among different virtual machines in the cloud. Based on the network performance model, we develop novel network performance aware algorithms for communication operations. This poster gives an overview of CMPI design, and presents some preliminary results on collective operations such as broadcast.We demonstrate the effectiveness of our network performance aware optimizations on Amazon EC2.

Infrastructure as a Service (IaaS) cloud has been attracting more and more customers as it provides the highest level of flexibility by offering configurable virtual machines (VMs) and computing infrastructures. Public VM images are usually available for customers to customize and launch. However, the 1 to N mapping between VM images and running instances in IaaS makes vulnerabilities propagate rapidly across the entire public cloud. Besides, IaaS cloud naturally comes with a larger and more stable attack surface and more concentrated target resources than traditional surroundings. In this paper, we first identify the threat of exploiting prevalent vulnerabilities over public IaaS cloud with an empirical study in Amazon EC2. We find that attackers can compromise a considerable number of VMs with trivial cost. We then do a qualitative cost-effectiveness analysis of this threat. Our main result is a two-fold observation: in IaaS cloud, exploiting prevalent vulnerabilities is much more cost-effective than traditional in-house computing environment, therefore attackers have stronger incentive; Fortunately, on the other hand, cloud defenders (cloud providers and customers) also have much lower cost-loss ratio than in traditional environment, therefore they can be more effective for defending attacks. We then build a game-theoretic model and conduct a risk-gain analysis to compare exploiting and patching strategies under cloud and traditional computing environments. Our modeling indicates that under cloud environment, both attack and defense become less cost-effective as time goes by, and the earlier actioner can be more rewarding. We propose countermeasures against such threat in order to bridge the gap between current security situation and defending mechanisms. To our best knowledge, we are the first to analyze and model the threat with prevalent known-vulnerabilities in public cloud.

Cloud computing is characterized by its essentially pay-per-use pricing with elasticity. Typically, the granularity of usage for such pricing is at virtual machine (VM) level in IaaS clouds, e.g., a multiple of machine hours. The elasticity and cost effectiveness in these clouds are primarily achieved through the exploitation of resource virtualization and sharing. However, a majority of applications running on VMs in clouds struggle to fully utilize resources allocated to them. Since co-location granularity is strictly restricted to VM level and resources allocated to VMs are space-shared, the unused resources are apt to be wasted while users are still charged for such wastage. In this paper, we address the problem of fine-grained and adaptive resource sharing for real pay-per-use pricing. To this end, we devise a resource management mechanism as a cost efficiency solution for both users and providers of clouds. The mechanism consists of a container-based resource allocator and a real-usage based pricing scheme. We demonstrate the efficacy of this mechanism via experiments, in Amazon EC2, using two typical workloads in clouds, web services and database services, and a compute-intensive high energy physics application. Our results show that the mechanism can achieve near-optimal cost efficiency.

Now a day’s most of the organizations depend on cloud infrastructure for application, storage and real time access perspective. Cloud based application save as a backbone to organization in terms of maintainability, scalability and management underlying infrastructure. To meet the demand of such organization cloud service providers provide Infra-Structure as a Service (IaaS) through Amazon EC2, Microsoft and Azure Virtual Machine. Because of higher utilization of cloud platform, it becomes more targeted to attackers. In security view of IaaS, malware become the most dangerous threat to IaaS Infrastructure. In proposed method, two RNN architectures of deep learning are considered for detecting malware in cloud virtual machine such as LSTMRNNs (Long Short Term RNN) and GRU (Gated Recurrent Unit RNN). Behavioral features of CPU, disk utilization and memory are learnt. Running applications in real time environment in online cloud platform with no restriction and capture all behaviors of normal and benign applications. This model achieves higher detection rate of 99% over 42,480 datasets. Based on the ordering of input data, the performance varied.

A fundamental problem that confronts virtual private network (VPN) applications is the overhead on throughput, ease of deployment and use, and overall utilization. WireGuard is a recently introduced light and secure cross-platform VPN application. It aims to simplify the process of setting up a secure connection while utilizing the multi-threading capability and minimizing the use of bandwidth. There have been several follow-up studies on WireGuard since its birth, most of which focus on the security analysis of the protocol. Despite the author's claim that WireGuard has impressive wins over OpenVPN and IPsec, there is no rigorous analysis of its performance to date. This paper presents a performance comparison of WireGuard and its main rival OpenVPN on various metrics. We construct an automated test framework and deploy it on a total of eight nodes, including remote AWS instances and local virtual machines. Our test results clearly show two main edges that WireGuard has over OpenVPN, its performance on multi-core machines and its light codebase.

Virtualization has been wide used for serving the ever growing computing demand, allowing cloud providers to instantiate multiple virtual machines (VMs) on a single set of physical resources. Customers use this shared resources without warned about the possibility of extracting or manipulating their sensitive data by an attacker who can co-resident his malicious VM with the target one. This paper presents the Co-residence Detection Technique (CDT), a method to analyze how attackers can co-resident with a target VM. Our method consists of three parts: (a) cartography cloud (b) co-residence test and (c) request for migration. We used Amazon EC2, GCE and Microsoft Azure as a case study to demonstrate that is possible to scan the local network and confirm co-residency with a target VM instance by using the network commands.

The increasing use of model-driven software development has renewed emphasis on using domain-specific models during application development. More specifically, there has been emphasis on using domain-specific modeling languages (DSMLs) to capture user-specified requirements when creating applications. The current approach to realizing these applications is to translate DSML models into source code using several model-to-model and model-to-code transformations. This approach is still dependent on the underlying source code representation and only raises the level of abstraction during development. Experience has shown that developers will many times be required to manually modify the generated source code, which can be error-prone and time consuming. An alternative to the aforementioned approach involves using an interpreted domain-specific modeling language (i-DSML) whose models can be directly executed using a Domain Specific Virtual Machine (DSVM). Direct execution of i-DSML models require a semantically rich platform that reduces the gap between the application models and the underlying services required to realize the application. One layer in this platform is the domain-specific middleware that is responsible for the management and delivery of services in the specific domain. In this dissertation, we investigated the problem of designing the domain-specific middleware of the DSVM to facilitate the bifurcation of the semantics of the domain and the model of execution (MoE) while supporting runtime adaptation and validation. We approached our investigation by seeking solutions to the following sub-problems: (1) How can the domain-specific knowledge (DSK) semantics be separated from the MoE for a given domain? (2) How do we define a generic model of execution (GMoE) of the middleware so that it is adaptable and realizes DSK operations to support delivery of services? (3) How do we validate the realization of DSK operations at runtime? Our research into the domain-specific middleware was done using an i-DSML for the user-centric communication domain, Communication Modeling Language (CML), and for microgrid energy management domain, Microgrid Modeling Language (MGridML). We have successfully developed a methodology to separate the DSK and GMoE of the middleware of a DSVM that supports specialization for a given domain, and is able to perform adaptation and validation at runtime.

The Cloud Computing is a most widely spreading platform for executing tasks using virtual machines (VMs) as processing elements. Therefore, implementing HPC using Cloud Computing is considered a powerful approach by isolating tasks, reducing execution time, as well as, price, and satisfying load balance. In this paper, an enhancement task scheduling algorithm on the Cloud Computing environment has been introduced to reduce the make-span, as well as, decrease the price of executing the independent tasks on the cloud resources. The principles of the algorithm is based on calculating the total processing power of the available resources (i.e., VMs) and the total requested processing power by the users' tasks, then allocating a group of users' tasks to each VM based on the ratio of its needed power relative to the total processing power of all VMs. The power of VMs has been defined based on Amazon EC2 and Google pricing models. To evaluate the performance of the enhancement algorithm, a comparative study has been done among this enhancement algorithm, the default FCFS algorithm, and the existed GA, and PSO algorithms. The experimental results show that the enhancement algorithm outperforms other algorithms by reducing make-span and the price of the running tasks.

The Amazon EC2 offers spot-priced virtual machines (VMs) at a reduced price compared to on-demand and reserved VMs. However, Amazon EC2 can terminate these VMs anytime due to the spot price and demand fluctuation. Using spot VMs results in a longer execution time and disrupts service availability. Users can use fault-tolerant techniques such as checkpointing, migration, and job duplication to mitigate the unreliability of spot VMs. In this paper, a knowledge-based checkpointing strategy is proposed to minimize the overall checkpointing overhead during the execution of jobs. The proposed scheme uses real-time price history to decide when to take a checkpoint. Results show that the proposed approach can significantly reduce the turnaround time by 18% compared to Hourly Checkpointing Strategy and 9% compared to Rising-Edge Checkpointing Strategy. One can also achieve 54% to 78% reliability with a cost saving of 78% for the workload used with the described approach.

Transient survivability analysis of a virtualized system (VS) is critical to the wide deployment of cloud services. The existing research of VS availability and/or reliability focused on the steady-state analysis. This paper presents a model and the closed-form solutions to analyze the survivability of both cloud service and VS after a service breakdown occurrence by using continuous-time Markov chain. Service breakdown may be caused by software rejuvenation of virtual machine (VM) and/or VM monitor (VMM), or caused by VM and/or VMM bugs. The VS applies two techniques for improving service survivability: VM failover and live VM migration. The proposed model and the defined survivability metrics not only enable us to quantitatively assess the system survivability but also provide insights on the investment efforts in system recovery strategies. Sensitivity analysis through numerical analysis is carried out to study the impact of key parameters on system survivability.

The rapid growth in cloud computing encourages the cloud provider to create more reliable, scalable and efficient cloud environment. For providing such a scalable environment, cloud provider develops large-scale Cloud Data Centers (CDC). Such CDCs consume a large amount of electrical energy for operational purpose and cooling purpose to keep the machines operating at a right temperature, but it also emits a large amount of carbon-dioxide. Thus to decrease the energy consumption, Virtual Machines (VMs) are consolidated to fewer numbers of active servers through VM migration techniques, this leads to Service Level Agreement (SLA) violation and performance degradation. Therefore to retain the energy-performance tradeoff, we should perform an optimum number of VM migrations. There are two cases when the VM migration performed: First when a hot spot is found and Second when a cold spot is found. But in both the cases we do not directly migrate the VM instead first we check whether it is required to migrate the VM. After that, the algorithm finds an appropriate destination host on which the VM will be migrated. In this paper, we implement three approaches to select destination host: First-fit, Best-fit, and Worst-fit. Finally compare the result of a number of VM migration performed, energy consumption and SLA violations with existing one.

In recent decades, task scheduling and load balancing in the cloud is a growing research area, due to the vast amount of data stored in the server highly increases the load. In order to address this concern, Hybrid Max-Min Genetic Algorithm (HMMGA) is proposed for task scheduling and load balancing in the cloud environment. At first, the load is evaluated for every Virtual Machine (VM), if the load is high, then HMMGA is used for balancing the load. HMMGA selects the best VMs to assign the tasks and migrates the over-loaded VMs tasks to the under-loaded VMs. HMMGA significantly avoids the imbalanced workload performance in the cloud environment. In this research paper, the proposed HMMGA performance is compared to Max-Min algorithm, Low time complexity and low cost binary Particle Swarm Optimizer (IBPSO-LBS) and PSO with Technique of Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm to examine the efficacy of HMMGA. From the experimental simulation, the result shows that HMMGA averagely delivers 1.63 and 3.88 seconds less make span compared to the Max-Min and TOPSIS-PSO algorithm for five VMs. In addition, HMMGA averagely enhances 10% to 40% of resource utilization than the MaxMin and TOPSIS-PSO algorithm. In another experiment, the HMMGA approximately showed 1.7 to 25.99 seconds less average waiting time compared to the Max-Min and IBPSO-LBS.

Cloud computing has brought a paradigm shift in computing world. Cloud Service Providers (CSP) use several pricing models for the services they offer. These models can be of short-term or long-term requirements for Cloud Service Users (CSU). Primary objective of our research is to minimize the total cost by finding optimal resource requirements to satisfy CSUs' demand. CSPs like Amazon EC2, a major Infrastructure as a Service (IaaS) provider uses pricing scheme like reserved, on-demand and spot instances for its Virtual Machine (VM) distribution. In this paper, we proposed a QoS aware heuristic approach to minimize job completion time and cost spot instances. In the proposed model, a heuristic approach is used for provisioning spot instances and an artificial neural network (ANN) is employed to predict the pricing spot instance, price which in turn enhances the performance and validate the quality of service. Efficacy of the proposed model is tested using Amazon EC2's real price traces and the total cost of CSUs' are compared with different approaches.

ABSTRACT: An agent-less technique to understand virtual machines (VMs) behavior and their changes during the VM life-cycle is essential for many performance analysis and debugging tasks in the cloud environment. Because of privacy and security issues, ease of deployment and execution overhead, the method preferably limits its data collection to the physical host level, without internal access to the VMs. We propose a host-based, precise method to recover execution flow of virtualized environments, regardless of the level of virtualization. Given a VM, the Any-Level VM Detection Algorithm (ADA) and Nested VM State Detection (NSD) Algorithm compute its execution path along with the state of virtual CPUs (vCPUs) from the host kernel trace. The state of vCPUs is displayed in an interactive trace viewer (TraceCompass) for further inspection. Then, a new approach for profiling threads and processes inside the VMs is proposed. Our proposed VM trace analysis algorithms have been open-sourced for further enhancements and to the benefit of other developers. Our new techniques are being evaluated with workloads generated by different benchmarking tools. These approaches are based on host hypervisor tracing, which brings a lower overhead (around 1%) as compared to other approaches.

Amazon's EC2 On-Demand marketspace is the dominant platform for trading Cloud services such as virtual machines. On such platforms consumers and providers do not negotiate with each other. Instead, consumers purchase predefined virtual machines at fixed-prices and so this approach is also termed take-it-or-leave-it approach. In the last years more dynamic platforms emerged such as Amazon's spot marketspace which was relaunched at the end of 2017 - here consumers can bid for virtual machines. The recent efforts of Amazon and other Cloud providers such as VirtuStream show that dynamic trading mechanisms are a promising approach for realizing future Cloud markets. Hence, multi-round bilateral negotiations which are executed autonomously have gained popularity in the scientific community. A key challenge towards the adoption of autonomous multi-round bilateral negotiations is to ensure the integrity and transparency so that the generated agreements are legally biding. In the paper at hand we present an approach which uses a smart contract - called Bazaar-Contract - to ensure integrity and transparency during multi-round bilateral negotiations. Thereby, consumers and providers exchange offers by calling methods of the Bazaar-Contract. Moreover, Cloud referees can use these Bazaar-Contracts in order to manage penalties resulting from poor service quality. In order to prove the technical feasibility of our approach we implemented the Bazaar-Contract using Ethereum and the Inter-Planetary File System. We evaluate the economical feasibility of our approach by considering the gas costs.